@InProceedings{bohm2005evolutionary,
  author = 	 {Niko B\"{o}hm and Gabriella K\'{o}kai and Stefan Mandl},
  title = 	 {{An Evolutionary Approach to Tetris}},
  booktitle =	 {Proceedings of The Sixth Metaheuristics International Conference},
  year =	 {2005},
  comment = 	 {Opisuje prvih 12 featurea koji se koriste za evaluaciju stanja Tetris ploče koji su spomenuti u opisu za chen2009apply.}
}

@MastersThesis{brzustowski1992can,
  author = 	 {John Brzustowski},
  title = 	 {{Can you win at TETRIS?}},
  school = 	 {The University of British Columbia},
  year = 	 {1992},
  abstract = 	 {TETRIS is a popular video game in which you try to fill rows in a rectangular well using a sequence of tetrominoes chosen by the machine. Each time you succeed in filling a row, it is deleted from the well. Your game ends when you have stacked pieces up to the top of the well. I build a model of TETRIS and analyze the worst-case scenario, in which the machine is treated as an adversary. I say you have a winning strategy when you can make your game last indefinitely. I construct winning strategies for some subsets of the TETRIS pieces, and prove that none exists for some others. Finally, I compare these analytic results to some empirical average-case data that I obtain from a passive survey of TETRIS players.},
  comment = 	 {Magistarski rad koji bi trebao opisati postojanje sekvence likova koja uvijek vodi na gubljenje TETRIS igrača.}
}

@Unpublished{carr2005applying,
  author = 	 {Donald Car},
  title = 	 {{Applying reinforcement learning to Tetris}},
  note = 	 {Department of Computer Science, Rhodes University, South Africa},
  year = 	 {2005},
  abstract = 	 {This paper investigates the possible application of reinforcement learning to Tetris. The author investigates the background of Tetris, and qualifies it in a mathematical context. The author discusses reinforcement learning, and considers historically successful applications of it. Finally the author discusses considerations surrounding implementation.},
  comment = 	 {Čini se kao neki neobjavljeni studentski rad, ali bi mogao poslužiti kao početak, možda ne za samo citiranje.}
}

@InProceedings{chen2009apply,
  author = 	 {Xingguo Chen and Hao Wang and Weiwei Wang and Yinghuan Shi and Yang Gao},
  title = 	 {{Apply Ant Colony Optimization to Tetris}},
  booktitle =	 {Proceedings of the 11th Annual conference on Genetic and evolutionary computation},
  pages =	 {1741--1742},
  year =	 {2009},
  abstract = 	 {Tetris is a falling block game where the player's objective to arrange a sequence of different shaped tetrominoes smoothly in order to survive. In the intelligence games, agent imitates the real player and chooses the best move based on a linear value function. In this paper, we apply Ant Colony Optimization (ACO) method to learn the weights of the function, trying to search an optimal weight-path in the weight graph. We use dynamic heuristic to prevent premature convergence to local optima. Our experimental result is better than most of traditional reinforcement learning methods.},
  comment = 	 {Opisuje tetris kao optimizaciju procjene stanja, gdje se optimiraju težine featurea (12 preuzetih + 4 nova predložena). Cilj rada, a moguće i općenitog istraživanja u Tetrisu, je kako opisati kvalitetu pojedinog stanja i onda se optimira taj opis, pa računalo može isprobati za novi potez sva moguća stanja i prema toj mjeri odrediti najbolje. Ponešto suprotan od našeg cilja, gdje bi mi trebali opisati zapravo konačnu funkciju, a ne tražiti ju, te onda probati naučiti mrežu da u određenom broju koraka proba ju smanjiti ili povećati (ovisno kako definiramo ju).}
}

@Article{demaine2008tetris,
  author = 	 {Erik D. Demaine and Susan Hohenberger and David Liben-Nowell},
  title = 	 {{Tetris is Hard, Even to Approximate}},
  journal = 	 {Computing and Combinatorics},
  year = 	 {2008},
  pages =	 {351--363},
  abstract = 	 {In the popular computer game of Tetris, the player is given a sequence of tetromino pieces and must pack them into a rectangular gameboard initially occupied by a given configuration of filled squares; any completely filled row of the gameboard is cleared and all pieces above it drop by one row. We prove that in the offline version of Tetris, it is NP-complete to maximize the number of cleared rows, maximize the number of tetrises (quadruples of rows simultaneously filled and cleared), minimize the maximum height of an occupied square, or maximize the number of pieces placed before the game ends. We furthermore show the extreme inapproximability of the first and last of these objectives to within a factor of p1−ε , when given a sequence of p pieces, and the inapproximability of the third objective to within a factor of 2 − ε, for any ε > 0. Our results hold under several variations on the rules of Tetris, including different models of rotation, limitations on player agility, and restricted piece sets.},
  comment = 	 {Članak koji bi trebao dokazivati da je tetris NP-potpun te da su aproksimacije tetrisa NP-teške.}
}

@Article{tesauro1995temporal,
  author = 	 {Gerald Tesauro},
  title = 	 {{Temporal Difference Learning and TD-Gammon}},
  journal = 	 {Communications of the ACM},
  year = 	 1995,
  volume =	 38,
  number =	 3,
  pages =	 {58--68},
  comment = 	 {Članak o korištenju reinforcement learninga za učenje blackgammona.}
}

